PresageIQ · Methodology

How PresageIQ
actually works.

PresageIQ produces business intelligence by converging multiple independent data streams — public records, live consumer signals, municipal licensing data, academic research, and AI synthesis — into a single, quantified picture of market opportunity. Rather than relying on any one signal, our composite scoring model weights six dimensions of business health, drawing evidence from Google Maps reviews, Census demographic profiles, KC municipal licensing records, GDELT news feeds, Reddit community discussions, and 22 curated KC media sources. The result is an Opportunity Score that reflects the actual competitive reality of a specific neighborhood, for a specific business type, at a specific moment — not a generic market survey or a consultant's intuition.

Six Scoring Dimensions

What goes into the score.

Each client analysis produces a composite Opportunity Score from 0 to 100. Every dimension is scored independently by AI — drawing on the specific evidence available for that business — then combined using fixed weights that never change. The math is deterministic. The AI only scores; it never decides the weights.

25%
of composite
Customer Sentiment
The pattern of customer satisfaction, praise, and complaint from publicly available reviews. Reflects both the lived experience of visiting this business and its reputation among active customers over time.
Google Maps Reviews Star Ratings Review Sentiment Analysis Editorial Summary
20%
of composite
Competitive Position
How this business stands relative to competitors within a 5-mile radius — by rating, review volume, price tier, and market differentiation. A 4.2★ in a field of 3.8★ competitors tells a different story than the same rating in a field of 4.6★ peers.
Google Places Nearby Competitor Ratings Price Tier Comparison AI Competitive Analysis
20%
of composite
Demand Alignment
The degree to which the business's menu, offerings, and identity match what customers actually want — evidenced by what they photograph, mention in reviews, and search for. A gap between what's being photographed and what's being marketed is a direct revenue signal.
Photo Vision AI Menu Extraction Review Item Mentions Website Scraping
15%
of composite
Operational Consistency
The reliability and consistency of service, kitchen execution, and staff performance — as revealed through patterns in customer reviews over time. Recurring complaints about wait times, inconsistent quality, or management issues are weighted differently than isolated incidents.
Review Text Patterns Operational Flags AI Profile Synthesis
10%
of composite
Digital Presence
The accessibility and quality of the business's digital footprint — website availability, online menu, Google Maps photo count, and social media discoverability. A business with no website and six photos signals a different digital investment than one with a maintained online presence.
Website Detection Menu Page Presence Photo Count Instagram Scraping Foursquare Metadata
10%
of composite
Community Signal
The organic presence of the business in local community conversations — Reddit posts, KC press coverage, GDELT news mentions, and community database records. This captures early signals — buzz before a review is written, a closing before a news story runs — that formal platforms miss.
Reddit KC Feeds GDELT News KC Press Sources Google Custom Search

The composite is computed deterministically, not by AI. Once each dimension receives its 0–100 score, the weighted average is calculated mathematically using fixed weights (25 + 20 + 20 + 15 + 10 + 10 = 100%). AI scores each dimension independently — it never decides how dimensions are weighted against each other. Scores above 75 are considered strong, 55–74 moderate, and below 55 at risk.

Data Sources

Where the data comes from.

Every signal in a PresageIQ analysis is sourced from one of eight data layers. Each has a defined update frequency, access method, and specific role in the analysis. Nothing is fabricated or estimated — if a data point isn't available, it's flagged as absent, not guessed.

Loading data sources…
Historical Disinvestment Layer

Why history belongs in a business score.

Market conditions in Kansas City are not solely the product of current consumer behavior or recent investment. In many neighborhoods, the competitive landscape, property values, capital access, and demographic composition are still shaped — measurably — by decisions made in the 1930s and 1940s. PresageIQ surfaces this history explicitly, because ignoring it would make our analysis less accurate, not more objective.

What redlining was. Between 1935 and 1940, the federal Home Owners' Loan Corporation (HOLC) commissioned security maps for 239 American cities, including Kansas City. Neighborhood appraisers assigned each area a letter grade — A ("Best"), B ("Still Desirable"), C ("Definitely Declining"), or D ("Hazardous"). Grade D neighborhoods were colored red on the maps — hence "redlining." Banks and federal mortgage programs used these grades to deny lending to residents and business owners in red and yellow zones, systematically cutting off access to capital in working-class, immigrant, and Black neighborhoods.

What it did to Kansas City. The consequences were not temporary. Neighborhoods graded D or C in 1940 received less public infrastructure investment, experienced accelerated housing decline, and saw residents denied the wealth-building mechanisms — homeownership, small business loans, commercial credit — available to their counterparts in A and B zones. The 18th and Vine Jazz District, one of the most culturally significant commercial corridors in KC history, was graded D. The Westside, Crossroads, and East Side neighborhoods were redlined despite active economic and cultural life. The effects of that capital deprivation compounded over 80 years.

Why it belongs in a business intelligence score. A business operating in a historically redlined neighborhood today inherits a specific market context: lower average property values, reduced generational wealth among residents, historically lower commercial rents but also historically lower foot traffic investment. It also often inherits a neighborhood in active revitalization — where the gap between historical disinvestment and current energy creates genuine opportunity. PresageIQ surfaces this context so our analysts and clients understand the terrain they're actually operating in, not a sanitized version of it.

The Mapping Inequality project at the University of Richmond's Digital Scholarship Lab has digitized and georeferenced every HOLC security map, making this historical data accessible and citable. When a client's neighborhood carries a C or D grade, PresageIQ notes the specific grade, year, and historical description in the Opportunity Score breakdown.

Kansas City HOLC Grades — 1939–1940
Source: Mapping Inequality, University of Richmond DSL
BrooksideA
Country Club PlazaA
Overland ParkA
Martin CityB
WaldoB
Downtown KCC
Hyde ParkC
MidtownC
North KCC
WestportC
18th and VineD
Argentine / RosedaleD
Crossroads Arts DistrictD
East Side / ProspectD
KCK / Wyandotte CountyD
River MarketD
WestsideD
A — Best
B — Still Desirable
C — Declining
D — Hazardous
How PresageIQ Uses This Data
When a client's neighborhood carries a C or D HOLC grade, the Opportunity Score breakdown includes a historical context block citing the specific grade, survey year, and a plain-English description of what the designation meant and what structural effects it produced. This appears alongside — not in place of — the numeric score, so analysts can interpret the score in light of the terrain it reflects.

The HOLC note reads: "Historical context applied: [Neighborhood] received HOLC grade [X] in [year], [description]. Source: Mapping Inequality."
Citation: Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., "Mapping Inequality," American Panorama, ed. Robert K. Nelson and Edward L. Ayers, accessed 2024. dsl.richmond.edu/panorama/redlining